Comparison of risk scores for the prediction of 30-day mortality after TAVI on the basis of the MIMIC-IV database

doi:10.21203/rs.3.rs-4868967/v1

The aim of this study was to investigate the baseline characteristics of patients admitted to the ICU after TAVI and the ability of six scores (Acute physiology score III(APSIII), Sequential Organ Failure Assessment(SOFA), Logistic Organ Dysfunction Score(LODS), Systemic Inflammatory Response Syndrome Score(SIRS), oxford acute severity of illness score(OASIS) and Simplified Acute Physiological Score II(SAPSII)) for the severity of ill in critically illness patients on the first day in the ICU with regard to the predict 30-day mortality risk. Data regarding patients who were undergoing TAVI were extracted from the database of MIMIC-IV.The AUCs for APSIII, SOFA, LODS, SIRS, OASIS and SAPSII were 0.882, 0.791, 0.882, 0.669, 0.845 and 0.774. Among the six scoring systems, the OASIS score had the highest sensitivity at 90.5%, while the LODS score had the highest specificity at 83.5%. Clinical decision curves revealed that APSIII had the largest area under the curve, and a slightly better net benefit within the threshold. In intensive care patients with TAVI, APSIII may be a better indicator of short-term mortality.

TAVI

APSIII

LODS

SOFA

MIMIC-IV database

There is an increase in the incidence of aortic valve disease every year, and surgical valve replacement is an effective treatment option[1]. TAVI (transcatheter aortic valve implantations) and SAVR (surgical aortic valve replacement) are the most common surgical procedures[1, 2]. Traditional heart valve replacement surgery does not offer the same advantages as TAVI. Faster recovery occurs because of less invasiveness[3]. In spite of this, however, the procedure is complex and short-term mortality remains high[4] .Risk assessment of cardiovascular disease is frequently done using STS scores and EuroScore II scores[5, 6]. Both scores may underestimate the risks of TAVR patients, according to some studies[6]. Therefore, it is crucial to identify a validated score.

Commonly used scores for disease severity in the ICU include the APSIII (Acute physiology score III), LODS (Logistic Organ Dysfunction Score), SIRS (Systemic Inflammatory Response Syndrome Score), SOFA (Sequential Organ Failure Assessment), SAPSII (Simplified Acute Physiological Score II), and OASIS (oxford acute severity of illness score) [7–11]. The intensity of illness after heart surgery varies from that of other surgical procedures. Studies have shown that scores are useful in predicting mortality among patients in intensive care units[12–15]. It is unknown, however, whether these scores can predict outcome in patients with post-TAVI intensive care. The objective of this study was to assess the accuracy of six scoring systems in predicting 30-day mortality in patients who are critically ill and have undergone TAVI. This study aims to provide valuable insights for clinical practice on the basis of these findings.

Database

This retrospective study utilized the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset[16] .According to the database, investigators had to pass an examination on 'Protecting Human Research Participants' on the NIH website and sign a data use agreement. A member of the team, Meiling Lu, passed the exam and was granted access (Record ID: 11818066).

This study was conducted in accordance with the tenets of the Helsinki Declaration. This study did not require informed consent or ethical approval.

Data analysis and statistical methods

Data extraction and study population identification were accomplished by screening the 'long_title' field of the 'mimiciv_hosp.procedures_icd' table of the MIMIC-IV database for patients that underwent TAVI surgery. In view of the fact that patients may be admitted to the ICU more than once, we included only the length of the first ICU stay for each patient.

Data extraction using PostgreSQL software: age, sex, weight, ethnicity, length of ICU stay, length of hospitalization, vital signs (respiratory rate, heart rate, DBP, MBP,SBP, temperature, and SpO2), coexisting comorbidities (chronic pulmonary disease, congestive heart failure, myocardial infarct, peripheral vascular disease, cerebrovascular disease, dementia, diabetes, rheumatic disease, renal disease, peptic ulcer disease, liver disease, malignant cancer ), laboratory tests (RBC 、RDW 、MCH 、MCHC 、MCV 、hematocrit 、hemoglobin 、platelet 、WBC 、PT 、PTT 、glucose 、AG 、bicarbonate 、Bun 、Ca²⁺、Cl⁻、Cr、 Na⁺、K⁺), scoring systems (APSIII,GCS,CCI,LODS,SAPSII,SOFA, OASIS, and SAPSII), dialysis, mechanical ventilation, vasoactive agents. Laboratory data and vital signs were extracted within 24 hours of ICU admission. Owing to potential bias, missing values for parameters greater than 20% were excluded. The mean of the nonmissing values was used to interpolate missing data in continuous variables.

Normality of continuous variables was assessed using the Kolmogorov-Smirnov test. If the variables were normally distributed, they were presented as mean ± standard deviation (M ± SD). Independent samples t-test were used for comparisons. When the distribution was not normal, we used the Mann-Whitney U test and presented the continuous variables as median interquartile ranges (IQRs). Categorical variables are expressed as percentages and numbers. Chi-squared tests and Fisher's exact tests were used for analysis. Z-tests were used to compare the AUCs of the subjects' working characteristic (ROC) curves.[17]Perform a goodness-of-fit test. In order to determine the net benefit of the six scoring systems, a decision curve analysis (DCA) was carried out[18, 19] .

Baseline characteristics

The study included 445 patients, 21 of whom died and 424 survived. Tables 1, 2, and 3 show a comparison of patient characteristics. Significantly higher SBP, bicarbonate, Cl, and GCS scores were observed in the surviving group than in the deceased group (p < 0 .05). In contrast, patients who died had significantly higher heart rates, respiratory rates, INR, PT, AG, glucose, BUN, Cr, CCI, ASPIII, LODS, SIRS, SOFA, SAPSII, and OASIS scores than those who survived. In comparison to the group that survived, the group that did not survive had a significantly greater prevalence of renal disease and likelihood of developing sepsis.

Baseline data

Variables	Survivor	non-survivor	p.overall
Total (n = 445)	*N=424*	*N=21*
Age(years)	83.0 [77.0;87.2]	81.0 [77.0;87.0]	0.653
Gender(male):	219 (51.7%)	13 (61.9%)	0.487
Race(white):	354 (83.5%)	17 (81.0%)	0.764
Weight(Kg)	75.8 [65.0;86.9]	75.4 [69.4;89.8]	0.781
Vital signs
Temperature (℃)	36.7 [36.6;36.9]	36.7 [36.4;36.9]	0.784
Heart rate(bpm)	74.4 [67.6;82.9]	81.6 [74.8;96.1]	0.006
Respiratory rate (cpm)	18.6 [16.9;20.2]	20.4 [18.2;23.6]	0.027
MBP (mmHg)	74.0 [69.3;79.2]	72.0 [70.3;73.7]	0.396
SBP (mmHg)	124 [116;134]	115 [107;121]	0.003
DBP (mmHg)	51.5 [45.9;56.5]	53.6 [51.4;58.0]	0.065
SpO2(%)	97.2 [96.1;98.2]	98.0 [96.2;98.5]	0.326
Cerebrovascular disease:	67 (15.8%)	7 (33.3%)	0.063
Chronic pulmonary disease:	163 (38.4%)	6 (28.6%)	0.497
Congestive heart failure:	327 (77.1%)	20 (95.2%)	0.058
Diabetes:	152 (35.8%)	10 (47.6%)	0.389
Liver:	22 (5.19%)	3 (14.3%)	0.106
Myocardial infarct:	98 (23.1%)	8 (38.1%)	0.190
Renal disease:	158 (37.3%)	14 (66.7%)	0.013
Rheumatic disease:	32 (7.55%)	2 (9.52%)	0.670
Dialysis active:	8 (1.89%)	1 (4.76%)	0.355
Sepsis3:	154 (36.3%)	14 (66.7%)	0.010

bpm：beat per minute; DBP：diastolic blood pressure; MBP：mean blood pressure; SBP: systolic blood pressure; cpm : count per minute; SpO2：saturation of peripheral oxygen;

Table 1 Comparison of baseline information between patients in the survival and death groups Laboratory data

Variables	Survivor	non-survivor	p.overall
Total (n = 445)	*N=424*	*N=21*
Hematocrit	30.4 [27.5;33.5]	29.5 [26.3;32.0]	0.248
Hb (g/dL)	9.90 [8.93;11.0]	9.50 [8.15;10.4]	0.143
RBC(10¹²/L)	3.35 [2.98;3.69]	3.26 [2.88;3.55]	0.430
RDW(%)	14.9 [13.9;16.2]	15.3 [14.8;16.6]	0.095
MCH(pg)	30.0 [28.5;31.3]	29.6 [28.8;31.4]	0.607
MCHC(%)	32.6 [31.7;33.5]	32.0 [31.4;32.8]	0.077
MCV(fL)	92.0 [87.6;96.0]	92.0 [87.6;98.0]	0.758
Plt(K/ L)	162 [128;200]	150 [132;192]	0.865
WBC（10⁹/L）	8.65 [7.01;11.6]	10.4 [8.50;13.2]	0.067
INR	1.20 [1.10;1.30]	1.30 [1.13;1.60]	0.010
PT	13.0 [12.1;13.9]	14.4 [12.1;17.3]	0.026
PTT	39.7 [31.9;58.1]	34.8 [31.8;53.5]	0.329
AG(mmol/L)	13.5 [11.9;15.0]	16.7 [14.3;18.0]	<0.001
Cr (mg/dL)	1.04 [0.80;1.35]	1.55 [1.10;2.80]	0.001
Bicarbonate(mmol/L)	24.5 [22.6;26.0]	22.3 [18.5;25.3]	0.022
BUN (mg/dL)	20.7 [16.0;29.0]	44.5 [24.7;66.0]	<0.001
K(mEq/L)	4.05 [3.90;4.30]	4.23 [3.95;4.60]	0.160
Na(mEq/L)	138 [136;140]	137 [134;140]	0.362
Cl(mmol/L)	104 [101;106]	102 [97.0;105]	0.044
Ca(mmol/L)	8.48 [8.15;8.70]	8.40 [8.10;8.71]	0.782
Glucose(mg/dL)	124 [110;147]	150 [130;188]	0.009

RBC: red blood cell ; RDW: red cell distribution width ； Hb: Hemoglobin; MCV: mean corpuscular volume; MCH: mean corpuscular hemoglobin; MCHC: mean corpuscular hemoglobin concentration; PLT: platelet; WBC: white blood cells; AG: anion gap ； BUN： blood urea nitrogen; Ca2+: calcium; Cl-: chloride; Cr: creatinine; Na+: sodium; K+: potassium; PT: prothrombin time; PTT: partial thromboplastin time ；

Table 2 Comparison of laboratory data between patients in the survival and nonsurviving groups Scoring data

Variables	Survivor	non-survivor	p.overall
Total (n = 445)	*N=424*	*N=21*
CCI	7.00 [6.00;9.00]	9.00 [8.00;10.0]	<0.001
GCS	15.0 [14.0;15.0]	9.00 [6.00;15.0]	<0.001
APSIII	38.0 [31.8;48.0]	65.0 [57.0;87.0]	<0.001
LODS	3.00 [1.00;4.25]	8.00 [6.00;11.0]	<0.001
OASIS	27.0 [23.0;32.0]	37.0 [33.0;45.0]	<0.001
SAPSII	33.0 [29.0;40.0]	46.0 [37.0;54.0]	<0.001
SIRS	2.00 [1.00;3.00]	3.00 [2.00;4.00]	0.006
SOFA	3.00 [2.00;5.00]	7.00 [5.00;10.0]	<0.001

GCS: Glasgow coma scale; CCI :Charlson comorbidity index; LODS: logistic organ dysfunction system; SOFA : sequential organ failure assessment ；SIRS: systemic inflammatory response syndrome score; OASIS: Oxford acute severity of illness score； SAPS II: simplified acute physiology score; APSIII: acute physiology score III.

Table 3 Comparison of scoring data between patients in the survival and death groups

	APSIII	LODS	OASIS	SAPSII	SIRS	SOFA
AUC	0.88	0.88	0.85	0.77	0.67	0.79
cutoff	52	6	31	36	3	6
sensitivity	0.857	0.857	0.905	0.857	0.524	0.667
specificity	0.814	0.835	0.677	0.578	0.715	0.781
C-indexes	0.882	0.882	0.845	0.771	0.669	0.791
Brier	0.035	0.033	0.037	0.042	0.043	0.039
p-value	<0.0001	<0.0001	<0.0001	<0.0001	<0.0019	<0.0001
z-value	6.010	6.170	5.530	4.510	3.110	5.310

Table 4 Model evaluation of the 6 scoring systems

		p-value	95%CI
APSIII
	LODS	0.996	-0.063~0.063
	OASIS	0.182	-0.017~0.09
	SAPSII	0.003	0.039~0.183
	SIRS	0.004	0.07~0.356
	SOFA	0.019	0.015~0.167
LODS
	APSIII	0.996	-0.063~0.063
	OASIS	0.243	-0.025~0.098
	SAPSII	0.003	0.037~0.184
	SIRS	0.004	0.07~0.356
	SOFA	0.058	-0.003~0.184
OASIS
	APSIII	0.182	-0.017~0.09
	LODS	0.243	-0.025~0.098
	SAPSII	0.034	0.006~0.143
	SIRS	0.005	0.054~0.299
	SOFA	0.164	-0.022~0.131
SAPSII
	APSIII	0.003	0.039~0.183
	LODS	0.003	0.037~0.184
	OASIS	0.034	0.006~0.143
	SIRS	0.22	-0.061~0.266
	SOFA	0.719	-0.13~0.09
SIRS
	APSIII	0.004	0.07~0.356
	LODS	0.004	0.07~0.356
	OASIS	0.005	0.054~0.299
	SAPSII	0.22	-0.061~0.266
	SOFA	0.052	-0.246~0.001

Table 5 The 6 scores were compared via the Delong test

Comparison of ROC curves

Figure 1 shows the ROC curves for the six scores used to predict 30-day mortality after TAVI, while Fig. 2 presents the AUC visualization. The AUCs for ASPIII, SOFA, LODS, SIRS, OASIS and SAPSII were 0.882, 0.791, 0.882, 0.669, 0.845 and 0.774. The boundary value corresponding to the maximum cut-off point of the Youden index for the six scoring systems was chosen as the diagnostic cut-off point for predicting patient death. Among the six scoring systems, the OASIS score had the highest sensitivity at 90.5%, while the LODS score had the highest specificity at 83.5% (Table 4). Differences in the AUC values of the six scoring systems were analyzed via a Z -test. In Table 5, the results suggest that the APSIII score and the LODS score have the same AUC value and that the test results are not significantly different (P = 0.996). The AUC value of the APSIII score was significantly larger than the SIRS, SAPSII and sofa score (P = 0.004; P = 0.003;P = 0.019). The AUC value of the APSIII score was larger than that of the OASIS score, but the difference was not statistically significant (P = 0.182). The AUC value of the LODS score was significantly larger than the SIRS score and SAPSII score (P = 0.004; P = 0.003). The AUC value of the LODS score was greater than that of the OASIS score and SOFA score, but the difference was not statistically significant (P = 0.243; P = 0.058). The AUC values of the OASIS score were greater than those of the SOFA score, but the difference was not statistically significant (P = 0.164). The AUC values were greater for the SOFA score than for the SIRS and SAPSII scores (P = 0.052; P = 0.719), but not statistically significant. The AUC value for the SIRS score was the smallest and not statistically significant compared to the SAPSII score (P = 0.22).

Evaluation of effectiveness

The p-value of the goodness-of-fit test for all six scores was greater than 0.05. The decision curves in Fig. 3 demonstrate that the APSIII line is almost always higher than the other colored lines (in descending order: APSIII, LODS, OASIS, SOFA, SAPSII, and SIRS). This suggests that the APSIII has the greatest range of net benefits among the five scoring systems.

In 2002, the first transcatheter aortic valve implantation (TAVI) was performed[20]. The use of TAVI throughout the world has increased since then. In the beginning, TAVI was intended for patients with high-risk aortic stenosis who were not candidates for surgical aortic valve replacement (SAVR). Indications have evolved to include intermediate-risk and low-risk aortic stenosis patients as a result of technological advancements[21–23]. Studies have shown that this minimally invasive intervention offers the advantages of reduced trauma and faster recovery¹. Despite this, the procedure is complex and results in a high short-term mortality rate[4].

In this study, six scoring systems were evaluated for predicting short-term mortality in patients in the intensive care unit undergoing TAVI. The area under the curve (AUC) for both APSIII and LODS was 0.882, the highest among the scoring systems. The overall severity and acuity of illness score (OASIS) had the greatest sensitivity, and the LODS score had the greatest specificity. When the APSIII score was compared with the LODS score, no significant difference was found between the two groups (p > 0.05). Clinical decision curves revealed that APSIII had the largest area under the curve, and a slightly better net benefit within the threshold.

Introduced in 1996, the Sequential Organ-Failure Assessment (SOFA) was created to track alterations in organ function among patients.[10] The diagnostic criteria for sepsis in 2016 incorporated the SOFA score[24]. SOFA scores can be used to predict mortality in ICU patients[25]. Le Gall et al.[8] developed the SAPS-II score in 1993 to improve on the SAPS-I score. SAPS-II scores may be more effective than SOFA scores for predicting patient mortality, according to some studies.[26]A new score called OASIS was introduced by Johnson et al[11]that uses machine learning algorithms to optimize the APACHE IV score by including variables that can be easily identified by the users. Studies have shown that the OASIS score is as predictive for ICU patients as the SAPS-II score in the short term. Furthermore, the OASIS score is more accurate at predicting the risk of death in ICU patients than the SOFA score[14]. In comparison to other scores, the Le Gall et al.[9] LODS (1996) score has a higher proportion of neurological components. According to the study, LODS scores were more predictive of ICU outcomes than OASIS scores[15]. The SIRS score was introduced by Bone[7]in 1992 and was later used as a prediction tool for death after some researchers found it was useful[27] .The Acute Physiology and Chronic Health Evaluation III is a scoring system developed by the European Society of Critical Care Medicine and the American Academy of Critical Care Medicine to assess the severity and prognosis of critically ill patients. In the article by Zhu et al.[28], the area under the curve (AUC) and 95% confidence interval (CI) for APS III and LODS were 0.84 (0.83–0.85) and 0.81 (0.80–0.82), respectively. The APS III or LODS scores can be employed to forecast the 28-day mortality risk in extracardiac ICU settings. This finding is consistent with our study.

This study has several limitations:(1) Preoperative cardiac function classification, markers of myocardial injury, STS scores, and EuroScore 2 scores could not be accurately obtained because of the limited data extracted from the MIMIC-IV; (2) between 2008 and 2019, the indications for TAVI have changed, making it difficult to assess any resulting bias. This was a single-center retrospective study, which may be subject to unavoidable bias. Thus, prospective studies are needed to validate the results of this study.

In conclusion, of the six scoring systems, the APSIII score has excellent predictive value and greater clinical benefit. In intensive care patients undergoing TAVI, it may be a more accurate predictor of short-term mortality.

Ethical Statement: All procedures performed in this study were in accordance with the Helsinki Declaration. Study group member Meiling Lu gained access by passing the Protecting Human Research Participants exam on the NIH website and signing a data use agreement (record ID: 11818066). This study did not require informed consent or ethical approval.

Author Contributions:(I) Conception and design: ML and PW;(II) Administrative support: ML;(III) Provision of study materials or patients: ML and PW;(IV) Collection and assembly of data: ML and PW;(V) Data analysis and interpretation: ML and PW;(VI) Manuscript writing: All authors (VII) Final approval of manuscript: All authors.

Competing Interests: On behalf of all the authors, the corresponding author states that there is no conflict of interest.

Acknowledgements: Thanks to the MIMIC-IV database for providing free data.

Data availability statement ：The data presented in this study were obtained from the publicly accessible MIMIC-IV database. The MIMIC database can be accessed at https://mimic.physionet.org/.

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No competing interests reported.

Comparison of risk scores for the prediction of 30-day mortality after TAVI on the basis of the MIMIC-IV database

Status:

Version 1

Abstract

Figures

Introduction

Methods

Database

Data analysis and statistical methods

Results

Baseline characteristics

Comparison of ROC curves

Evaluation of effectiveness

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1